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Found 4 papers, 2 papers with code
Learning Dynamical Systems Encoding Non-Linearity within Space Curvature
By learning the manifold's Euclidean embedded representation, our approach encodes the non-linearity of the DS within the curvature of the space.
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents.
Implicit Manifold Gaussian Process Regression
Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets.
Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps
We study the eigenvectors and eigenvalues of the Graph Laplacian and show that they form a set of orthogonal embedding spaces, one for each sub-dynamics.
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